Statistically optimized biopsy strategy for the diagnosis of prostate cancer

Dinggang Shen, Zhiqiang Lao, Jianchao Zeng, Edward H. Herskovits, Gabor Fichtinger, Christos Davatzikos

Research output: Contribution to journalArticlepeer-review


This paper presents a method for optimizing prostate needle biopsy, by creating a statistical atlas of the spatial distribution of prostate cancer from a large patient cohort. In order to remove inter-individual morphological variability and to determine the true variability in the spatial distribution of cancer within the prostate, an adaptive-focus deformable model (AFDM) is first used to register and normalize the prostate samples. A probabilistic method is then developed to select the prostate-biopsy strategy that the greatest chance of detecting prostate cancer. For a test set of data from 20 prostate subjects, five needle locations are adequate to detect the tumor 100% of the time. Furthermore, the results on the accuracy of deformable registration and the predictive power of our statistically optimized biopsy strategy are presented in this paper.

Original languageEnglish (US)
Pages (from-to)433-438
Number of pages6
JournalProceedings of the IEEE Symposium on Computer-Based Medical Systems
StatePublished - Jan 1 2001

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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